scipy stats.betaprime() | Python

Parameters : q : lower and upper tail probability a, b : shape parameters x : quantiles loc : [optional]location parameter. Default = 0 scale : [optional]scale parameter. Default = 1 size : [tuple of ints, optional] shape or random variates. moments : [optional] composed of letters [‘mvsk’]; 'm' = mean, 'v' = variance, 's' = Fisher's skew and 'k' = Fisher's kurtosis. (default = 'mv'). Results : beta prime continuous random variable

# importing scipy
from scipy.stats import betaprime

numargs = betaprimeprime.numargs
[a, b] = [0.6, ] * numargs
rv = betaprimeprime(a, b)

print ("RV : \n", rv)

RV : 
 <scipy.stats._distn_infrastructure.rv_frozen object at 0x0000029482FCC438>

import numpy as np
quantile = np.arange (0.01, 1, 0.1)
 
# Random Variates
R = betaprime.rvs(a, b, scale = 2,  size = 10)
print ("Random Variates : \n", R)

# PDF
R = betaprime.pdf(quantile, a, b, loc = 0, scale = 1)
print ("\nProbability Distribution : \n", R) 

Random Variates : 
 [ 1.59603917  1.92408727  1.2120992   0.34064091  2.68681773 22.99956678
  1.45523032  2.93360219 23.93717261 18.04203815]

Probability Distribution : 
 [2.58128122 0.8832351  0.61488062 0.47835546 0.39160163 0.33053737
 0.28490363 0.24941484 0.22101038 0.1977718 ]
 

import numpy as np
import matplotlib.pyplot as plt

distribution = np.linspace(0, np.minimum(rv.dist.b, 5))
print("Distribution : \n", distribution)

plot = plt.plot(distribution, rv.pdf(distribution))

Distribution : 
 [0.         0.10204082 0.20408163 0.30612245 0.40816327 0.51020408
 0.6122449  0.71428571 0.81632653 0.91836735 1.02040816 1.12244898
 1.2244898  1.32653061 1.42857143 1.53061224 1.63265306 1.73469388
 1.83673469 1.93877551 2.04081633 2.14285714 2.24489796 2.34693878
 2.44897959 2.55102041 2.65306122 2.75510204 2.85714286 2.95918367
 3.06122449 3.16326531 3.26530612 3.36734694 3.46938776 3.57142857
 3.67346939 3.7755102  3.87755102 3.97959184 4.08163265 4.18367347
 4.28571429 4.3877551  4.48979592 4.59183673 4.69387755 4.79591837
 4.89795918 5.        ]

from scipy.stats import arcsine
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 1.0, 100)

# Varying positional arguments
y1 = betaprime.pdf(x, 2.75, 2.75)
y2 = betaprime.pdf(x, 3.25, 3.25)
plt.plot(x, y1, "*", x, y2, "r--")